Sharing photos

ABSTRACT

Implementations generally relate to sharing photos. In some implementations, a method includes collecting photos associated with one or more objects, where the photos are collected from a plurality of users. The method also includes collecting attention information associated with the one or more objects. The method also includes generating an attention map based on the attention information. The method also includes grouping the one or more photos into groups of photos based on the attention map. The method also includes causing the groups of photos to be displayed to a target user based on one or more predetermined criteria.

BACKGROUND

Digital cameras have made taking photos easy and convenient, and variousapplications have made sharing photos easy and convenient. For example,some applications enable a person to instantly upload photos to a socialnetwork system as photos are captured. Many people capturing photos ofthe same object are often positioned in the same or similar location.Accordingly, in many situations, numerous photos of the same object arecaptured, where the photos are substantially the same or are slightvariations of each other.

SUMMARY

Implementations generally relate to sharing photos. In someimplementations, a method includes collecting photos associated with oneor more objects, where the photos are collected from a plurality ofusers. The method also includes collecting attention informationassociated with the one or more objects. The method also includesgenerating an attention map based on the attention information. Themethod also includes grouping the one or more photos into groups ofphotos based on the attention map. The method also includes causing thegroups of photos to be displayed to a target user based on one or morepredetermined criteria.

With further regard to the method, one or more of the photos arecaptured using one or more respective devices that are operable to tracka gaze of a user. In some implementations, the attention information isbased on tracking gazes of the plurality of users. In someimplementations, the method further includes enabling the plurality ofusers to have access to the groups of photos. In some implementations,at least one group of photos includes photos of at least one object fromdifferent perspectives. In some implementations, at least one group ofphotos includes photos that receive a level of attention that meets apredefined attention threshold. In some implementations, at least onegroup of photos includes one or more photos of at least one person whois socially connected to the target user in a social network. In someimplementations, at least one group of photos includes photos of atleast one object that the target user has captured. In someimplementations, the attention map is based on tracking gazes of one ormore of the plurality of users. In some implementations, the generatingof the attention map includes: receiving gaze information; identifyingthe one or more objects; associating the gaze information with each ofthe one or more objects; and determining an attention value for each ofthe one or more objects based on the gaze information.

In some implementations, a method includes collecting photos associatedwith one or more objects, where the photos are collected from aplurality of users, and where one or more of the photos are capturedusing one or more respective devices that are operable to track a gazeof a user. In some implementations, the method further includescollecting attention information associated with the one or moreobjects, where the attention information is based on tracking gazes ofthe plurality of users. In some implementations, the method furtherincludes generating an attention map based on the attention information.In some implementations, the method further includes grouping the one ormore photos into groups of photos based on the attention map, where atleast one group of photos includes photos that receive a level ofattention that meets a predefined attention threshold. In someimplementations, the method further includes causing the groups ofphotos to be displayed to a target user based on one or morepredetermined criteria.

In some implementations, a system includes one or more processors, andlogic encoded in one or more tangible media for execution by the one ormore processors. When executed, the logic is operable to performoperations including: collecting photos associated with one or moreobjects, where the photos are collected from a plurality of users;collecting attention information associated with the one or moreobjects; generating an attention map based on the attention information;grouping the one or more photos into groups of photos based on theattention map; and causing the groups of photos to be displayed to atarget user based on one or more predetermined criteria.

With further regard to the system, one or more of the photos arecaptured using one or more respective devices that are operable to tracka gaze of a user. In some implementations, the attention information isbased on tracking gazes of the plurality of users. In someimplementations, the logic when executed is further operable to performoperations including enabling the plurality of users to have access tothe groups of photos. In some implementations, at least one group ofphotos includes photos of at least one object from differentperspectives. In some implementations, at least one group of photosincludes photos that receive a level of attention that meets apredefined attention threshold. In some implementations, at least onegroup of photos includes one or more photos of at least one person whois socially connected to the target user in a social network. In someimplementations, at least one group of photos includes photos of atleast one object that the target user has captured. In someimplementations, the attention map is based on tracking gazes of one ormore of the plurality of users.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of an example network environment,which may be used to implement the implementations described herein.

FIG. 2 illustrates an example simplified flow diagram for sharing photosamong users, according to some implementations.

FIGS. 3A, 3B, 3C, and 3D illustrate example simplified photos of objectsfrom different vantage points.

FIG. 4 illustrates an example diagram of an eye tracking device thattracks the gaze of a user, according to some implementations.

FIG. 5 illustrates an example simplified flow diagram for generating anattention map, according to some implementations.

FIG. 6 illustrates an example diagram of an attention map, according tosome implementations.

FIG. 7 illustrates an example simplified flow diagram for groupingphotos based on an attention map, according to some implementations.

FIG. 8 illustrates a block diagram of an example server device, whichmay be used to implement the implementations described herein.

DETAILED DESCRIPTION

Implementations described herein facilitate the sharing of photos amongmultiple people. In various implementations, the system facilitates thesharing of the photos based on an attention map. For example, the systemmay group and present collected photos of objects based on how muchattention particular objects have received relative to other objects.This facilitates the overall photo sharing process among users, in thatthe system informs users of how popular particular objects are in thephotos.

In some implementations, the system collects photos from multiple users,where one or more of the photos are captured using one or morerespective devices that are operable to track a gaze of a user. Thesystem then collects attention information associated with the one ormore objects, where the attention information is based on tracking gazesof the users. The system then generates an attention map based on theattention information. In various implementations, the attention mapshows how much attention particular objects have received relative toother objects.

As indicated above, the system facilitates the sharing of the photosbased on an attention map. More specifically, the system groups one ormore photos into various groups of photos based on how much attentionparticular objects have received relative to other objects. For example,the system may group photos based on different levels of attention thatobjects have received. In some implementations, the system may groupphotos of objects that have received a particular level of attentionfrom one or more people with whom a target user is socially connected ina social network. In some implementations, the system displays groups ofphotos to the target user based on various criteria associated withattention levels. The system then enables the target user to have accessto the groups of photos.

FIG. 1 illustrates a block diagram of an example network environment100, which may be used to implement the implementations describedherein. In some implementations, network environment 100 includes asystem 102, which includes a server device 104 and a social networkdatabase 106. The term system 102 and phrase “social network system” maybe used interchangeably. Network environment 100 also includes clientdevices 110, 120, 130, and 140, which may communicate with each othervia system 102 and a network 150.

For ease of illustration, FIG. 1 shows one block for each of system 102,server device 104, and social network database 106, and shows fourblocks for client devices 110, 120, 130, and 140. Blocks 102, 104, and106 may represent multiple systems, server devices, and social networkdatabases. Also, there may be any number of client devices. In otherimplementations, network environment 100 may not have all of thecomponents shown and/or may have other elements including other types ofelements instead of, or in addition to, those shown herein.

In various implementations, users U1, U2, U3, and U4 may communicatewith each other using respective client devices 110, 120, 130, and 140.Users U1, U2, U3, and U4 may also use respective client devices 110,120, 130, and 140 to take photos. In various implementations, clientdevices 110, 120, 130, and 140 may include any types of electronicdevices such as mobile phones (e.g., smart phones), tablets, notebookcomputers, desktop computers, digital cameras, etc. Such client devices110, 120, 130, and 140 that are not dedicated digital cameras mayinclude integrated digital cameras.

In various implementations, system 102 may utilize an eye trackingdevice for collecting attention information, where the eye trackingdevice may be used in conjunction with a camera device, which may be adedicated digital camera or a digital camera integrated with anelectronic device (e.g., any of client devices 110, 120, 130, 140,etc.). The eye tracking device may itself be integrated with any one ormore of client devices 110, 120, 130, 140, etc. As described in moredetail below, such an eye tracking device may be any suitable eyetracking device that measures eye positions such as the point of gaze(e.g., the user's line of sight) and/or measures eye movement.

In some implementations, client devices 110, 120, 130, and 140 mayinclude wearable computing devices, including any hands-free devices.For example, in some implementations, one or more client devices mayinclude devices that operate with a head-mounted camera, head-mountedeye tracking device, and/or head-mounted display.

FIG. 2 illustrates an example simplified flow diagram for sharing photosamong users, according to some implementations. Referring to both FIGS.1 and 2, a method is initiated in block 202, where system 102 collectsphotos associated with one or more objects, where the photos arecollected from multiple users. For example, system 102 may collectphotos of the same object from many different people, from manydifferent vantage points.

FIGS. 3A, 3B, 3C, and 3D illustrate example simplified photos of objectsfrom different vantage points. FIG. 3A shows two pyramids 304 and 306 ina desert setting. FIG. 3B shows the same two pyramids 304 and 306 from afurther distance as shown in FIG. 3A. FIG. 3B also shows a tree 302.FIGS. 3C and 3D show two pyramids 304 and 306 from different vantagepoints.

For ease of illustration, FIGS. 3A, 3B, 3C, and 3D show four differentvantages points, and these vantage points are significantly distinct.Many other vantage points are possible, depending on the particularscenario. Furthermore, many people capturing photos of the same objectare often positioned in the same or similar location. For example, twopeople could be standing next to each other while capturing photos ofthe same object. In another example, two people could take turnsstanding in the same spot to capture photos of the same object.Accordingly, in many situations, numerous photos of the same object aresubstantially the same or slight variations of each other. Suchvariations in photos may be due to gaze differences (e.g., differencesin height among people, etc.), camera differences (e.g., differences inquality, settings, etc.), lighting differences (e.g., differences intime of day, weather, etc.), background and environmental differences(e.g., variations in the sky, clouds, airplanes, etc.), foregrounddifferences (e.g., people, friends, family, etc.), etc.

In block 204, system 102 collects attention information associated withthe one or more objects. The one or more objects may be set in any givenvisual context. For example, a given object may be a pyramid in a desertsetting, as shown in the examples of FIGS. 3A, 3B, 3C, and 3D. Otherexamples may include a statue in a plaza, a particular person's face ina group of people, etc.

In various implementations, attention information characterizes theattention (e.g., focus, fixation points, etc.) of a given user withrespect to a particular object during any given point in time. Invarious implementations, attention information may include anycombination of attention values, attention parameter values, and gazeinformation, which are described in more detail below.

In some implementations, gaze information may include various gazeparameter values associated with pitch, yaw, roll, line of sight, fieldof view, etc. In various implementations, one or more of the photos arecaptured using one or more respective devices that are operable to trackthe gaze (e.g., line of sight) of a user. Devices that are operable totrack a gaze facilitate system 102 in determining values of gazeparameters such as pitch, yaw, roll, line of sight, field of view, etc.For example, a given device while capturing photos may also capture gazeinformation. Such an eye tracking device may be any suitable eyetracking device that measures the point of gaze (e.g., the user's lineof sight). Example implementations of a device that is operable to trackthe gaze of a user are described in more detail below in connection withFIG. 4.

For ease of illustration, some implementations are described herein inthe context of the gaze of a single user. These implementations andothers also apply to gazes of multiple users. For example, for a givenobject (e.g., a pyramid), system 102 may track, log, and aggregate thegaze information of multiple users. As such, in some implementations,attention information is based on tracking gazes of one or more users.Example implementations involving attention information are described indetail below in connection with FIG. 5.

In block 206, system 102 generates an attention map based on theattention information. The attention map shows how much attentionparticular objects have received relative to other objects. In variousimplementations, the attention map is based on various aspects ofattention information. For example, the attention map may be based ontracking gazes of one or more of the users relative to one or moreobjects (e.g., gaze information), and/or based on any other aspect ofattention information such as attention values, attention parametervalues, etc.). Example implementations directed to generating anattention map are described in more detail below in connection with FIG.5.

In block 208, system 102 groups the one or more photos into groups ofphotos based on the attention map. For example, system 102 may groupphotos of a given object (e.g., a monument) or portion of an object thatis popular among tourists (e.g., receiving high attention from differentpeople). Example implementations directed to grouping photos into groupsof photos based on an attention map are described in more detail belowin connection with FIG. 7.

In block 210, system 102 causes the groups of photos to be displayed toa target user based on one or more predetermined criteria. For example,system 102 may cause the groups of photos to be displayed on the displayscreen of the user's mobile device. In various implementations, thecriteria may be based on attention thresholds. For example, in someimplementations, system 102 may display groups of photos of objects thathave an attention level that meets a predetermined attention threshold.In other words, system 102 may display to the target user groups of themost popular photos. In another example, the criteria may be based onsocial network aspects. For example, in some implementations, system 102may display to the target user groups of photos taken by users who aresocially connected to the target user. In another example, the criteriamay be based on objects themselves. For example, system 102 may displaygroups of photos showing objects that the target user has also capturedin photos. In various implementations, system 102 enables users to haveaccess to the groups of photos, where a given target user may viewdifferent groups of photos to select photos to access.

FIG. 4 illustrates an example diagram of an eye tracking device 400 thattracks the gaze of a user, according to some implementations. In someimplementations, eye tracking device 300 may be positioned in the headarea of the user. For example, as shown, eye tracking device 300 may bepositioned relatively close to an eye of the user.

In various implementations, eye tracking device 400 may use suitable eyetracking technologies, including any suitable eye-tracking hardwarecomponents and algorithms, to measure eye positions and eye movement.For example, eye tracking device 400 may use any suitable eye trackingtechnologies to measure the gaze of the user (e.g., the user's line ofsight) or the motion of an eye relative to the head. For example, eyetracking device 400 may use a laser and laser technology to measure eyepositions and eye movement relative to objects in the environment.

In some implementations, eye tracking device 400 may track the gaze ofthe user by tracking one or more parameters such as pitch, yaw, roll,line of sight, field of view, etc. FIG. 4 shows a pitch axis, yaw axis,and roll axis to illustrate how eye tracking device 400 may movedepending on eye movement and/or head movement of the user, as theattention that the user places on particular objects would influence andcorrelate to both eye movement and head movement of the user.

FIG. 4 shows example objects 302, 304, and 306, where object 302 is atree, object 304 is a large pyramid, and object 306 is a small pyramid.As shown, objects 302, 304, and 306 are in user's line of site 408 andin the user's field of view 410. For ease of illustration, FIG. 4 showsthree objects 302, 304, and 306. Any given scene in the user's field ofview 410 may include any number of objects, including people.

In some implementations, system 102 may receive gaze information (e.g.,parameter values associated with tracked parameters) directly from eyetracking device 400 or from any other one or more suitable storagelocations. For example, in some implementations, eye tracking device 400may send gaze information to system 102 as the user gazes at particularobjects. In some implementations, when used with a camera device, eyetracking device 400 may send gaze information to system 102 as a cameradevice sends photos to system 102 (e.g., as the photos are captured). Insome implementations, eye tracking device 400 may store gaze informationlocal to the user's client device (e.g., if used with a dedicateddigital camera, or if used with a mobile phone or other electronicdevice that has an integrated digital camera, etc.).

FIG. 5 illustrates an example simplified flow diagram for generating anattention map, according to some implementations. Referring to FIGS. 1,4, and 5, a method is initiated in block 502, where system 102 receivesgaze information. In some implementations, the gaze informationassociated with a given user may also be referred to as the gaze patternof the user, or the gaze of the user. As indicated above, the gazeinformation may include one or more parameter values associated withgaze parameters such as pitch, yaw, roll, line of sight, field of view,etc.

In some implementations, where photos are provided by users and wherethe photos are associated with the gaze information, system 102 may alsoassociate geotags with both the photos and gaze information. System 102may include such geotags in the gaze parameters. In someimplementations, where photos are not provided, system 102 may includegeographical coordinates from gaze information, and include suchgeographical coordinates with the gaze parameters

In block 504, system 102 identifies objects based on the gazeinformation. In some implementations, system 102 may receive one or morephotos of one or more objects. System 102 may then identify objects ineach photo via any suitable object identification algorithm.

In some implementations, system 102 may also recognize the identifiedobjects. For example, after system 102 identifies an object such as amonument or face, system 102 may then apply a suitable recognitionalgorithm to recognize an identity associated with the particular object(e.g., monument or particular person). Example implementations directedto object recognition are described in more detail below.

In some implementations, system 102 may receive gaze informationindependently of photos being captured. In some implementations, system102 may also identify a given object based on the gaze information evenbefore the user captures a photo of the object or even if the user doesnot ultimately capture a photo of the object. Accordingly, system 102may capture gaze information even if the user is not concurrentlycapturing photos. In various implementations, system 102 may track onlygaze information without recognizing which user is providing the gazeinformation. In other words, gaze information may be anonymous. In someimplementations, a given user may want system 102 to recognizing thegiven user as the user providing the gaze information in order to enablesystem 102 in providing individual and/or customized services for theuser. As described in more detail below, system 102 enables users tospecify and/or consent to the use of personal information, includinggaze information.

In block 506, system 102 associates the gaze information with each ofthe one or more objects receiving the attention of the user. Forexample, in some implementations, the gaze information (e.g., thecombination of gaze parameter values) characterizes the gaze of theuser. System 102 may determine, from the gaze information, fixationpoints on one or more objects in a given photo, including fixationpoints at particular portions of such objects.

Note that a photo need not necessarily be provided by the userassociated with the gaze information. System 102 may determine anappropriate photo based on geolocation information, other photosprovided by the user associated with the gaze information, etc. In someimplementations, where system 102 aggregates gazes from multiple users,system 102 may associate the gaze information from the different userswith the same one or more objects. For example, referring to FIG. 4, ifmultiple users gaze at object 304, system 102 may associate the gazeinformation associated with all of such users with the same object 304.

In block 508, system 102 determines an attention value for each of theone or more objects based on the gaze information. In someimplementations, the attention value may be based on one or moreattention parameter values. Such attention parameters may include, forexample, the amount of time a given user gazed at a given object. Insome implementations, such attention parameters have correspondingattention subvalues that system 102 may aggregate in order to derive agiven attention value. In various implementations, the phrase “attentionparameter value” may be use interchangeably with the phrase “attentionsubvalue.”

In some implementations, system 102 may assign an attention subvaluethat is proportional to the total amount of time that the user gazed atthe object. For example, system 102 may assign a higher attentionsubvalue if the user gazed at the object for 10 minutes versus only 2minutes.

In some implementations, system 102 may assign an attention subvaluethat is proportional to the total number of times that the user gazed ata given object. For example, system 102 may assign a higher attentionsubvalue if the user gazed at the object 5 different times versus asingle time.

In some implementations, system 102 may assign an attention subvaluethat is proportional to the total size and/or percentage of a givenobject at which the user gazed. For example, system 102 may assign ahigher attention subvalue if the user gazed at 75% of the object versus25% of the object.

In some implementations, system 102 may assign an attention subvaluethat is proportional to the total number of people who gazed at a givenobject. For example, system 102 may assign a higher attention subvalueif 1,000 people gazed at the object versus 5 people. Other attentionparameters are possible, and the particular number of attentionparameters and the types of parameters will depend on the particularimplementation.

In block 510, system 102 generates an attention map based on theattention values. As indicated above, the attention map may be based onattention information in that the attention information may in turn bebased on attention values. An example attention map is described in moredetail below in connection with FIG. 6.

FIG. 6 illustrates an example diagram of an attention map 600, accordingto some implementations. As indicated above, an attention map shows howmuch attention particular objects have received relative to otherobjects. As shown, objects 302, 304, and 306 are shown with Xs overlaid,where the number of Xs is proportional to the attention value. In someimplementations, objects 302, 304, and 306 may each be shown with anactual attention value. For example, as shown in this exampleimplementation, object 302 has an attention value of 9, object 304 hasan attention value of 97, and object 306 has an attention value of 85.The range of attention values may vary (e.g., 0 to 1.0; 0 to 100; 1 to1,000, etc.), and the particular range and/or numbering scheme willdepend on the particular implementation.

In some implementations, system 102 may assign a color to each object,where the particular color may correspond to the size of the attentionvalue. For example, an object associated with yellow may have arelatively higher attention value than an object associated with blue;an object associated with orange may have a relatively higher attentionvalue than an object associated with yellow; an object associated withred may have a relatively higher attention value than an objectassociated with orange. The particular color scheme will vary, dependingon the particular implementation.

FIG. 7 illustrates an example simplified flow diagram for groupingphotos based on an attention map, according to some implementations. Asdescribed in more detail below, system 102 group photos into variousgroups of photos based on the attention map in order to facilitate usersin sharing photos with each other.

In some implementations, a method is initiated in block 702, wheresystem 102 determines one or more attention thresholds. System 102 mayassociate predetermined attention thresholds proportionally with levelsof attention. For example, system 102 may associate a relatively lowerattention threshold with a relatively lower level of attention.Conversely, system 102 may associate a relatively higher attentionthreshold with a relatively higher level of attention. The actual numberof predetermined thresholds may vary, and will depend on the particularimplementation.

In block 704, system 102 determines an attention value for each of theone or more objects. Various implementations directed to attentionvalues are described above in connection with FIG. 5.

In block 706, system 102 compares the attention value for each objectagainst one or more predetermined attention thresholds.

In block 708, system 102 determines if the attention value for eachobject meets one or more of the predetermined attention thresholds. Insome implementations, an attention value meets a given attentionthreshold if the attention value is greater than the attentionthreshold. In some implementations, an attention value meets a givenattention threshold if the attention value is greater than or equal tothe attention threshold. Also, a given attention value may meet multipleattention thresholds. In some implementations, system 102 may associatethe attention value with the highest attention threshold met for thepurposes of grouping photos for sharing among users. The particularcriteria for meeting an attention threshold may vary and will depend onthe particular implementation.

In block 710, system 102 groups the photos based on the attention map.More specifically, system 102 groups one or more photos into variousgroups of photos based on how much attention particular objects havereceived relative to other objects. In some implementations, system 102may group photos based on different levels of attention that objects inthe photos have received. For example, a given group of photos mayinclude photos that receive a level of attention that meets a firstpredefined attention threshold (e.g., very popular). Another group ofphotos may include photos that receive a level of attention that meets asecond predefined attention threshold (e.g., somewhat popular).

In some implementations, system 102 may group photos of one or moreobjects that have received a particular level of attention, where thephotos show the one or more objects from different perspectives. In someimplementations, the system may group photos of objects that havereceived a particular level of attention from one or more people withwhom the target user is socially connected in a social network.

In some implementations, the system may group photos of objects thathave received a particular level of attention, where the photos includeobjects that the target user has captured in photos.

In various implementations, system 102 may create multiple predeterminedattention thresholds corresponding with multiple levels of attention(e.g., levels of popularity) among users. As such, system 102 may groupphotos into different groups associated with different levels ofpopularity for the user to consider when accessing photos. As a result,system 102 enables users to access a large pool of photos of the sameobject(s) based on various attention levels associated with theobject(s).

Although the steps, operations, or computations described herein may bepresented in a specific order, the order may be changed in particularimplementations. Other orderings of the steps are possible, depending onthe particular implementation. In some particular implementations,multiple steps shown as sequential in this specification may beperformed at the same time. Also, some implementations may not have allof the steps shown and/or may have other steps instead of, or inaddition to, those shown herein.

While system 102 is described as performing the steps as described inthe implementations herein, any suitable component or combination ofcomponents of system 102 or any suitable processor or processorsassociated with system 102 may perform the steps described.

In various implementations, system 102 may utilize a variety ofrecognition algorithms to recognize faces, landmarks, objects, etc. inphotos. Such recognition algorithms may be integral to system 102.System 102 may also access recognition algorithms provided by softwarethat is external to system 102 and that system 102 accesses.

In various implementations, system 102 enables users to specify and/orconsent to the use of personal information. Use of personal informationmay include, for example, system 102 using their faces in photos orusing their identity information in recognizing people identified inphotos, and may also include system 102 tracking the gaze of a user evenwhen the user is not capturing photos. In some implementations, system102 may provide users with multiple selections directed to specifyingand/or consenting to the use of personal information. For example,selections with regard to specifying and/or consenting may be associatedwith individual photos, all photos, individual photo albums, all photoalbums, etc. The selections may be implemented in a variety of ways. Forexample, system 102 may cause buttons or check boxes to be displayednext to various selections. In some implementations, system 102 enablesusers to specify and/or consent to the use of using their photos forfacial recognition in general. Example implementations for recognizingfaces and other objects are described in more detail below.

In various implementations, system 102 obtains reference images ofusers, where each reference image includes an image of a face that isassociated with a known user. The user is known, in that system 102 hasthe user's identity information such as the user's name and otherprofile information. In some implementations, a reference image may be,for example, a profile image that the user has uploaded. In someimplementations, a reference image may be based on a composite of agroup of reference images.

In some implementations, to recognize a face in a photo, system 102 maycompare the face (i.e., image of the face) and match the face toreference images of users. Note that the term “face” and the phrase“image of the face” are used interchangeably. For ease of illustration,the recognition of one face is described in some of the exampleimplementations described herein. These implementations may also applyto each face of multiple faces to be recognized.

In some implementations, system 102 may search reference images in orderto identify any one or more reference images that are similar to theface in the photo. In some implementations, for a given reference image,system 102 may extract features from the image of the face in a photofor analysis, and then compare those features to those of one or morereference images. For example, system 102 may analyze the relativeposition, size, and/or shape of facial features such as eyes, nose,cheekbones, mouth, jaw, etc. In some implementations, system 102 may usedata gathered from the analysis to match the face in the photo to onemore reference images with matching or similar features. In someimplementations, system 102 may normalize multiple reference images, andcompress face data from those images into a composite representationhaving information (e.g., facial feature data), and then compare theface in the photo to the composite representation for facialrecognition.

In some scenarios, the face in the photo may be similar to multiplereference images associated with the same user. As such, there would bea high probability that the person associated with the face in the photois the same person associated with the reference images.

In some scenarios, the face in the photo may be similar to multiplereference images associated with different users. As such, there wouldbe a moderately high yet decreased probability that the person in thephoto matches any given person associated with the reference images. Tohandle such a situation, system 102 may use various types of facialrecognition algorithms to narrow the possibilities, ideally down to onebest candidate.

For example, in some implementations, to facilitate in facialrecognition, system 102 may use geometric facial recognition algorithms,which are based on feature discrimination. System 102 may also usephotometric algorithms, which are based on a statistical approach thatdistills a facial feature into values for comparison. A combination ofthe geometric and photometric approaches could also be used whencomparing the face in the photo to one or more references.

Other facial recognition algorithms may be used. For example, system 102may use facial recognition algorithms that use one or more of principalcomponent analysis, linear discriminate analysis, elastic bunch graphmatching, hidden Markov models, and dynamic link matching. It will beappreciated that system 102 may use other known or later developedfacial recognition algorithms, techniques, and/or systems.

In some implementations, system 102 may generate an output indicating alikelihood (or probability) that the face in the photo matches a givenreference image. In some implementations, the output may be representedas a metric (or numerical value) such as a percentage associated withthe confidence that the face in the photo matches a given referenceimage. For example, a value of 1.0 may represent 100% confidence of amatch. This could occur, for example, when compared images are identicalor nearly identical. The value could be lower, for example 0.5 whenthere is a 50% chance of a match. Other types of outputs are possible.For example, in some implementations, the output may be a confidencescore for matching.

For ease of illustration, some example implementations described hereinhave been described in the context of a facial recognition algorithm.Other similar recognition algorithms and/or visual search systems may beused to recognize objects such as landmarks, logos, entities, events,etc. in order to implement implementations described herein.

Implementations described herein provide various benefits. For example,implementations described herein provide users with a large pool ofphotos for a given object. Implementations also increase the size of thepool and evolve the pool by guiding users to capture particular photosbased on an attention map.

FIG. 8 illustrates a block diagram of an example server device 800,which may be used to implement the implementations described herein. Forexample, server device 800 may be used to implement server device 104 ofFIG. 1, as well as to perform the method implementations describedherein. In some implementations, server device 800 includes a processor802, an operating system 804, a memory 806, and an input/output (I/O)interface 808. Server device 800 also includes a social network engine810 and a media application 812, which may be stored in memory 806 or onany other suitable storage location or computer-readable medium. Mediaapplication 812 provides instructions that enable processor 802 toperform the functions described herein and other functions.

For ease of illustration, FIG. 8 shows one block for each of processor802, operating system 804, memory 806, I/O interface 808, social networkengine 810, and media application 812. These blocks 802, 804, 806, 808,810, and 812 may represent multiple processors, operating systems,memories, I/O interfaces, social network engines, and mediaapplications. In other implementations, server device 800 may not haveall of the components shown and/or may have other elements includingother types of elements instead of, or in addition to, those shownherein.

Although the description has been described with respect to particularembodiments thereof, these particular embodiments are merelyillustrative, and not restrictive. Concepts illustrated in the examplesmay be applied to other examples and implementations.

Note that the functional blocks, methods, devices, and systems describedin the present disclosure may be integrated or divided into differentcombinations of systems, devices, and functional blocks as would beknown to those skilled in the art.

Any suitable programming languages and programming techniques may beused to implement the routines of particular embodiments. Differentprogramming techniques may be employed such as procedural orobject-oriented. The routines may execute on a single processing deviceor multiple processors. Although the steps, operations, or computationsmay be presented in a specific order, the order may be changed indifferent particular embodiments. In some particular embodiments,multiple steps shown as sequential in this specification may beperformed at the same time.

A “processor” includes any suitable hardware and/or software system,mechanism or component that processes data, signals or otherinformation. A processor may include a system with a general-purposecentral processing unit, multiple processing units, dedicated circuitryfor achieving functionality, or other systems. Processing need not belimited to a geographic location, or have temporal limitations. Forexample, a processor may perform its functions in “real-time,”“offline,” in a “batch mode,” etc. Portions of processing may beperformed at different times and at different locations, by different(or the same) processing systems. A computer may be any processor incommunication with a memory. The memory may be any suitableprocessor-readable storage medium, such as random-access memory (RAM),read-only memory (ROM), magnetic or optical disk, or other tangiblemedia suitable for storing instructions for execution by the processor.

What is claimed is:
 1. A method comprising: collecting photos associatedwith one or more objects, wherein the photos are collected from aplurality of users, and wherein one or more of the photos are capturedusing one or more respective devices that are operable to track a gazeof a user; collecting attention information associated with the one ormore objects, wherein the attention information is based on trackinggazes of the plurality of users; generating an attention map based onthe attention information; grouping the one or more photos into groupsof photos based on the attention map, wherein at least one group ofphotos includes photos that receive a level of attention that meets apredefined attention threshold; and causing the groups of photos to bedisplayed to a target user based on one or more predetermined criteria.2. A method comprising: collecting photos associated with one or moreobjects, wherein the photos are collected from a plurality of users;collecting attention information associated with the one or moreobjects; generating an attention map based on the attention information;grouping the one or more photos into groups of photos based on theattention map; and causing the groups of photos to be displayed to atarget user based on one or more predetermined criteria.
 3. The methodof claim 2, wherein one or more of the photos are captured using one ormore respective devices that are operable to track a gaze of a user. 4.The method of claim 2, wherein the attention information is based ontracking gazes of the plurality of users.
 5. The method of claim 2,further comprising enabling the plurality of users to have access to thegroups of photos.
 6. The method of claim 2, wherein at least one groupof photos includes photos of at least one object from differentperspectives.
 7. The method of claim 2, wherein at least one group ofphotos includes photos that receive a level of attention that meets apredefined attention threshold.
 8. The method of claim 2, wherein atleast one group of photos includes one or more photos of at least oneperson who is socially connected to the target user in a social network.9. The method of claim 2, wherein at least one group of photos includesphotos of at least one object that the target user has captured.
 10. Themethod of claim 2, wherein the attention map is based on tracking gazesof one or more of the plurality of users.
 11. The method of claim 2,wherein the generating of the attention map comprises: receiving gazeinformation; identifying the one or more objects; associating the gazeinformation with each of the one or more objects; and determining anattention value for each of the one or more objects based on the gazeinformation.
 12. A system comprising: one or more processors; and logicencoded in one or more tangible media for execution by the one or moreprocessors and when executed operable to perform operations comprising:collecting photos associated with one or more objects, wherein thephotos are collected from a plurality of users; collecting attentioninformation associated with the one or more objects; generating anattention map based on the attention information; grouping the one ormore photos into groups of photos based on the attention map; andcausing the groups of photos to be displayed to a target user based onone or more predetermined criteria.
 13. The system of claim 12, whereinone or more of the photos are captured using one or more respectivedevices that are operable to track a gaze of a user.
 14. The system ofclaim 12, wherein the attention information is based on tracking gazesof the plurality of users.
 15. The system of claim 12, wherein the logicwhen executed is further operable to perform operations comprisingenabling the plurality of users to have access to the groups of photos.16. The system of claim 12, wherein at least one group of photosincludes photos of at least one object from different perspectives. 17.The system of claim 12, wherein at least one group of photos includesphotos that receive a level of attention that meets a predefinedattention threshold.
 18. The system of claim 12, wherein at least onegroup of photos includes one or more photos of at least one person whois socially connected to the target user in a social network.
 19. Thesystem of claim 12, wherein at least one group of photos includes photosof at least one object that the target user has captured.
 20. The systemof claim 12, wherein the attention map is based on tracking gazes of oneor more of the plurality of users.